We need to sort out the facts from the myths and then do something about natural and fake AI risks.
Artificial intelligence (AI) has come a long way in a short amount of time. AI is changing everything from health care to banking, and it can make people more productive than ever before. But this exciting promise comes with the worry that the public and some experts are worried about the rise of “Killer AI.” In a world where innovation has already changed society in ways no one could have predicted, how do we tell which fears are real and which should be left to fiction?
We just published a policy paper for the Mercatus Center at George Mason University called “On Defining “Killer AI” to help answer these questions. In it, we offer a new way to evaluate AI systems based on how likely they are to cause harm. This is an essential step toward dealing with the problems AI poses and making sure it is used responsibly in society.
AI has already shown how powerful it can be by helping solve some of society’s biggest problems. It helps doctors make better decisions, speeds up scientific study, and makes business processes more efficient. AI frees up the human ability to work on more critical and creative tasks by taking over repetitive tasks.
There is a lot of good that can happen. Even though it’s hopeful, it’s not too hard to imagine an AI-powered economy in which, after a time of adjustment, people are much healthier, wealthier, and happier while working much less than they do now.
CAMERAS FROM INVISIBLE AI’S INTELLIGENT AGENTS ARE CAPABLE OF IDENTIFYING MISTAKES MADE BY AUTOMATIC WORKERS AND ROBOTS.
But it is essential to make sure that this potential is reached safely. Our attempt to figure out how dangerous AI is in the real world is also the first time we’ve tried to describe “Killer AI” in a complete way.
We describe it as AI systems that directly hurt or kill people, either on purpose or because of things they didn’t plan for. Notably, the term includes real and virtual AI systems and distinguishes between them. This is because different kinds of AI could cause harm.
Even though their examples are hard to understand, science fiction can help show how both real and virtual AI systems can cause actual physical harm. The Terminator figure has been used to show how dangerous natural AI systems can be for a long time. But virtual AI systems could be even more difficult. The newest “Mission Impossible” movie shows an extreme case of this. Realistically, we can say that the world is getting more and more linked, and our most critical infrastructure is no exception.
Our suggested framework is a way to evaluate AI systems in a structured way, with a critical focus on putting the needs of many people ahead of those of a few. By taking into account not just the chance of harm but also how bad it could be, we can thoroughly review AI systems’ safety and risk factors. It could help us find threats we hadn’t seen before and make it easier to deal with AI-related risks.
Our approach makes this possible by needing a more excellent knowledge of how an AI system could be reused or used wrong and the long-term effects of using an AI system. Also, we stress the importance of looking at stakeholders from different fields when thinking about these things. This will give us a more fair view of how these systems are made and put into place.
This review can be used as a starting point for complete laws and reasonable regulations, and talks about the ethics of Killer AI. Our focus on saving human lives and making sure everyone’s well-being is taken care of can help lawmakers answer and rank the most critical concerns raised by any possible Killer AIs.
Focusing on how important it is for multiple stakeholders from different fields to be involved might urge people from different backgrounds to get more involved in the talk. Through this, we hope that future laws will be more complete and the conversations around them will be more well-informed.
Even though the framework could be an essential tool for lawmakers, industry leaders, academics, and other stakeholders to use when evaluating AI systems, it also shows how important it is to do more study, look more closely, and be proactive in AI safety. This will be hard to do in an area that changes so quickly. Researchers will be encouraged by the fact that they can learn a lot from the technology.
AI should be a force for good, making people’s lives better instead of putting them in danger. By coming up with effective policies and ways to deal with the challenges of AI safety, society will be able to use this new technology to its fullest potential while protecting itself from possible harm. The structure shown here is a valuable tool for achieving this goal. Whether our worries about AI are genuine or not, we’ll be better off if we can figure out how to use this exciting new technology without making things worse.